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Visual Place Recognition (VPR) estimates the location of query images bymatching them with images in a reference database. Conventional methodsgenerally adopt aggregated CNN features for global retrieval and RANSAC-basedgeometric verification for reranking. However, RANSAC only employs geometricinformation but ignores other possible information that could be useful forreranking, e.g. local feature correlations, and attention values. In thispaper, we propose a unified place recognition framework that handles bothretrieval and reranking with a novel transformer model, named $R^{2}$Former.The proposed reranking module takes feature correlation, attention value, andxy coordinates into account, and learns to determine whether the image pair isfrom the same location. The whole pipeline is end-to-end trainable and thereranking module alone can also be adopted on other CNN or transformerbackbones as a generic component. Remarkably, $R^{2}$Former significantlyoutperforms state-of-the-art methods on major VPR datasets with much lessinference time and memory consumption. It also achieves the state-of-the-art onthe hold-out MSLS challenge set and could serve as a simple yet strong solutionfor real-world large-scale applications. Experiments also show visiontransformer tokens are comparable and sometimes better than CNN local featureson local matching. The code is released athttps://github.com/Jeff-Zilence/R2Former.

$R^{2}$Former: Unified $R$etrieval and $R$eranking Transformer for Place Recognition